CARDIFF SCHOOL OF SPORT
DEGREE OF BACHELOR OF SCIENCE
(HONOURS)
SPORTS COACHING
A PERFORMANCE PROFILE OF
SUCCESSFUL ONE DAY INTERNATIONAL
CRICKET TEAMS
JOE MAIDEN
ST09001610
Cardiff Metropolitan University
Prifysgol Fetropolitan Caerdydd
Certificate of student
I certify that the whole of this work is the result of my individual effort, that all
quotations from books and journals have been acknowledged, and that the word
count given below is a true and accurate record of the words contained (omitting
contents pages, acknowledgements, indexes, figures, reference list and
appendices).
Word count:
11,021
Signed:
Joe Maiden
Date:
07/03/2012
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Notes:
The University owns the right to reprint all or part of this document.
Table of Contents
List of Tables
List of Figures
Acknowledgments ................................................................................................................................. I
Abstract .................................................................................................................................................. II
CHAPTER ONE – INTRODUCTION................................................................................................. 1
CHAPTER TWO – LITERATURE REVIEW
2.1 – Initial Concepts ....................................................................................................................... 2
2.2 - Previous Cricket Research .................................................................................................... 3
2.3 – Justification of Study .............................................................................................................. 8
CHAPTER THREE - METHODS
3.1 – Research Design .................................................................................................................. 10
3.2 - System Development ........................................................................................................... 10
3.3- Matches (Participants/ Performances) ................................................................................ 11
3.4 – Data Gathering ..................................................................................................................... 12
3.5 – Data Analysis ...................................................................................................................... 133
3.6 – Reliability ............................................................................................................................... 14
CHAPTER FOUR - RESULTS
4.1 – General Overview................................................................................................................. 15
4.2 – Powerplay Comparison ....................................................................................................... 18
4.3 – Rebuilding Phase Comparison........................................................................................... 19
4.4– Non-Parametric Statistics..................................................................................................... 23
CHAPTER FIVE - DISCUSSION
5.1 – General Overview................................................................................................................. 25
5.2 – Powerplay Comparison ....................................................................................................... 27
5.3 – Rebuilding Phase Comparison........................................................................................... 28
5.4 – Non Parametric Statistics Comparison ............................................................................. 30
CHAPTER SIX - CONCLUSION ...................................................................................................... 32
CHAPTER SEVEN - REFERENCES .............................................................................................. 34
List of Tables
Table 1 – Comparison of bowling series statistics between spin and seam bowlers.
Page 17
Table 2 – Results from Mann Whitney U Test highlighting key areas of significance
(P>0.05). Page 24
List of Figures
Figure 1 - Function of NX Cricket Scorer application. Page 12
Figure 2 - An example of data output from series statistics. Page 12
Figure 3 - Comparison of runs scored by different areas of the batting line up. Page
16
Figure 4 - Comparison of overs bowled by spin and seam bowlers during the series.
Page 17
Figure 5 – Average dot balls faced during powerplay overs. Page 18
Figure 6 – Wickets taken during the powerplay overs. Page 18
Figure 7 – Bar chart identifying the use of spin or seam bowlers during the powerplay
overs. Page 19
Figure 8 – Comparison of average runs scored during the 5 over period after initial
wicket loss (Winning teams). Page 20
Figure 9 – Comparison of average runs scored during the 5 over period after initial
wicket loss (Losing teams). Page 20
Figure 10 – Average singles scored during a 5 over period after a wicket (Winning
teams) Page 21
Figure 11 – Average singles scored during the 5 over period after a wicket (Losing
teams) Page 22
Figure 12 – Average wickets lost during 5 over period after initial wicket (Winning
teams). Page 22
Figure 13 – Average wickets lost during 5 over period after initial wicket (Losing
teams). Page 23
Acknowledgments
I would like to thank Huw Wiltshire for his extensive support throughout
the whole analysis and writing up process. I would also like to thank
Adam Cullinane for supplying the Australia v Sri Lanka match footage for
my pilot study
I
Abstract
One day international cricket is becoming more accessible and a higher
requirement is placed on generating success. Previous research has
aimed at increasing awareness of individual performances however
hasn’t identified key team performance trends in successful teams. This
study aims to discover key areas of performance in successful one day
international cricket teams. In order to achieve this, four series during
the 2011-12 international season were observed and matches were
scored using an NX Cricket Scorer application on iPhone. The data that
was collected was analysed using a variety of non-parametric tests
including chi square and a Kruskal Wallis H test in order to observe
significant differences between successful and non successful sides,
and to also analyse patterns and trends between variables. On the back
of these tests the results that were deemed significant in successful
sides were highlighted and discussed in order to allow an understanding
of why they occurred and how they fitted in to key areas of successful
international performance.
The key findings from the study emphasised the effectiveness of spin
bowlers during series and during powerplay overs, demonstrating that
successful sides got more use from their spinners whilst remaining
aggressive and looking for wickets. Losing teams lost more wickets
during the powerplay overs showing that the successful sides tend not
only to take more wickets, but also use powerplay overs as periods of
stabilisation. It was found that winning teams faced less dot balls during
powerplay overs which allowed them to be more dynamic in rotating the
strike and not getting tied down. The findings that came from the study
II
tended to be specific for sub-continental sides, an area for future
research that came from this study would highlight whether the findings
were true for each continent and whether the key areas identified were
specific for successful sides in a worldwide context.
III
CHAPTER ONE – INTRODUCTION
One of the great characteristics of the game of cricket has been the careful
collection, archiving and publication of masses of figures in the form of statistics. In
spite of this extensive recording, relatively little use has been made of these statistics
to understand the nature of the game and the factors determining successful team
performances. After an apparent increase in the franchising and accessibility of
cricket as a global product, more emphasis is being put on the ability to achieve
success. Previous research categorised for statistical analysis in cricket it sparse
however a more conscious effort is being made now to appreciate what makes a
team successful and how their success can be sustained over a period of time.
In order to fully understand what makes a team successful a sufficient knowledge of
cricket from a performance base level and an understanding of performance analysis
techniques are required. By utilising both of these skills a comparison can be made
between key areas of performance and results can begin to be analysed on the
basis of what occurs in order for these results to occur.
Despite the wide range of factors affecting performance in cricket, analysts in the
cricket area tend to focus on predictive methods and studies which allow for statistics
to highlight individual player performances. (Lemmer 2002; 2003; 2006 & 2009;
Douglas et al 2010). Research in team performance in cricket is sparse as analysts
tend to focus on how individual players can influence match scenarios rather than
trends and patterns in team play which allow these instances to occur (Petersen
2008a; 2008b).
The need for constant adaptation of methods in cricket analysis has been highlighted
in the past (Petersen, 2008b). In order to get a current measurement of statistics,
performance indicators need to reflect the evolving nature of cricket from a
performance based perspective and need to be utilised in order to fully recognize
what the data represents.
The aim of this study is to primarily identify and draw attention to some key areas of
team performance which are replicable in all winning sides, and also look deeper into
these trends in order to understand why they might have occurred and how they can
be sustained. By achieving this, the study will be able to identify re-occurring
strengths in successful performance in order to create a profile for teams to follow
with a view to future success.
1
CHAPTER TWO – LITERATURE
REVIEW
2.1 – Initial Concepts
Performance analysis can play an important role when trying to ‘develop
understanding of sports that can inform decision making to enhance sports
performance’ (O’Donoghue, 2010). Performance analysis of sport is the
‘investigation of actual sports performance and is different from other disciplines as it
focuses on the actual performance rather than laboratory studies and questionnaires’
(O’Donoghue, 2010). Due to the vast amounts of statistics in the nature of cricket
quantitative research is used to generate data and results from performance. Jenkins
et al (2007) found that quantitative data provided statistical evidence of ‘broad areas
of team performance which required attention’ and produced a series of performance
indicators that were measurable against eachother in order to observe whether a
team is performing to a higher or lower level than usual. When looking at utilising
quantitative data in cricket research it is difficult to incorporate all performance
indicators of interest into a single results profile. Hughes et al (2001) aimed to
produce a technique for analysing multiple performances in sport however each
match was analysed in isolation meaning that the data didn’t produce generalised
patterns from an overall perspective from all of the games.
Typically a performance profile in performance analysis is ‘a collection of
performance indicators that together characterise typical performance’ (O’Donoghue,
2010). Performance profiling can be utilised effectively when analysing cricket.
Taylor et al (2008) found that ‘there are many sources of variability in sports
performance including opposition effects, venue and score line’ and further
concluded that teams and individuals needed to be measured and represented by
their performances from multiple matches. This study showed how analysis of
multiple matches can allow for a better representation of statistics and also showed
how a multi-match profile can be used to generate more data which allows for
patterns and trends to be observed. Morley & Thomas (2004) looked at
incorporating unforeseen factors into their data to analyse whether they had an effect
on the outcome of performances. They looked at cricket and whether home
advantage had an impact when looking at results. They did this by analysing the
English one day county league and identified 4 factors that they felt could have had
an effect. The factors they analysed were learning and familiarity with the
2
environment, travel factors, rule factors and crowd effects during matches. Their
research found that 57% of home teams won their games and concluded that these
results were found due to a mixture of the components that they analysed. Whilst
this didn’t look into a statistical analysis of cricket, it shows how researchers in the
cricket field have gone and found other methods and theories to try and understand
what makes up a successful cricket performance. O’Donoghue (2002) attempted to
use correlation techniques between outcome and process indicators in tennis. He
found that there were too many different styles of play to show any correlation
however stated that ‘modelling efforts to explore factors associated with successful
performances are an important area for further research’. This suggests that further
research into performance modelling can lead to factors affecting sports
performance being highlighted allowing for further investigations into the data to be
completed.
2.2 - Previous Cricket Research
The majority of cricket research occurs under quantitative discipline focusing on
statistical analysis of the game. Cricket can be a complex sport to look at statistically
as there are many variables that can affect the outcome of matches. Due to the
intricate nature of cricket it is important that performance indicators are clearly
defined. ‘A performance indicator is a selection or combination of action variables
that aim to define some or all aspects of performance’ (Hughes & Bartlett, 2002).
Performance indicators in cricket are often simple and defined under the three
aspects of the game (Petersen et al, 2008b). By breaking down performance
indictors into areas under the headings of bowling, batting and fielding it allows for
each area to be analysed in its own context. In spite of the complex nature of the
game of cricket, statistics remain the key aspect that is used to define player
performance (Petersen, 2008a). Petersen et al (2008b) concluded that ‘while
performance indicators in cricket were widespread, there is still little published
empirical data indicating their importance or usefulness in international cricket’. This
shows how in order to make the data that is gained from cricket research beneficial
for coaches, it is important to look deeper into what can be explored in areas that
previous studies may not have looked at. He also stated ‘one day cricket is always
evolving so the type of analysis will need updating at regular intervals’ (Petersen,
3
2008a) showing how due to the ever developing nature of cricket, some previous
research studies that were deemed as efficient may be seen as less effective when
looking at analysing the modern game.
Previous studies by Lemmer (2002; 2005 & 2006) have attempted to make use of
the vast amount of statistics in cricket and turn them into results that a coach can
make use of. During Lemmer’s studies he uses formulas to try to give a value to
statistics in order to forecast future performances. During his study (2002) he stated
that ‘researchers in cricket tend to focus predominantly on batting in international
cricket’ which led him to delve deeper into bowling research in order to come up with
his own interpretation of statistics (Lemmer 2002; 2005; 2006; 2008 & 2009).
Lemmer’s main objective was to create performance measures which allowed for
data to be more reliable and valid. An example of this was his study in 2002 which
looked at producing a combined bowling rate for bowlers. He did this by creating a
formula which allowed for a bowler to be rated against his other team mates and
opposition in terms of effectiveness in limited overs cricket. By doing this he allowed
for a general comparison amongst players to be generated which can benefit
coaches during selection. By providing a performance ranking it allows for coaches
to gain a better understanding of the statistics and allows them to make an informed
decision on all aspects of the data. This can justify being a valid technique in
analysing performance as it can highlight areas a team needs to work on, and allows
a coach to see what standard his players need to be at to improve the current team.
Lemmer further went on in 2005 to create a dynamic bowling rate for bowlers in
unlimited overs cricket which followed similar principles as his previous work but
gave recent performance data a higher value in order to determine current form and
ability. A lot of Lemmer’s work is based around creating new formulas which allow for
cricket statistics to be analysed more effectively. He went beyond looking at key
performance indicators and tried to identify areas that could be measured in more
detail in order to produce statistics that were match specific and offered an insight
into performance. Due to the quantitative nature of cricket research Lemmer’s
statistical work was fairly limited in what he could achieve. O’Donoghue (2010)
stated that although ‘quantitative data is often a good indicator of showing what
happens in sports performance, it doesn’t explain why the data occurred and why the
difference in variables may have affected the outcome’. This limits the nature of
4
cricket research as it can impact on the effectiveness of the data and shows that in
order to get more specific data it is important to go deeper into the statistics rather
than looking at them from their general values.
Initial studies by Lemmer (2002; 2006 & 2009) looked at identifying the key
performance indicators in bowling and creating a formula to compare bowlers and
their performances. By using over bowled, wickets taken and runs conceded
identified by (Petersen, 2008b) as key performance indicators in bowling, he created
a table of top performers and analysed how each bowler rated during singular
matches in their careers. Since then he has gone onto incorporate studies which
measure player performances at major tournaments (Lemmer 2008) and included
measures that have been developed in order to take into account the ability of the
opposition (Lemmer 2007; 2008b). These methods are justified as by creating a
better understanding of opposition effects the data becomes more valid and can be
utilised in a more efficient way. Lemmer’s studies aimed to produce valuable
statistics to benefit the selectors in South African cricket. He found that during his
studies and the values his analyses created, the selectors didn’t always succeed in
selecting the best players to play. His goal was to convince selectors and
statisticians to use his methods in order to ‘select the best players, measure their
performance on a regular basis and train them to reach and sustain their optimal
performance levels’ (Lemmer 2006). By incorporating his own methods, Lemmer
allowed for other researchers in cricket to use his ideas in order to interpret cricket
statistics for themselves.
Other researchers have used the methods identified by Lemmer and created their
own processes for the analysis of cricket statistics (Basevi & Binoy 2007). Their
study created new formulas adapted from Lemmers (2002) combined bowling rate
and Lemmers (2007) calculation of batting and bowling measures studies. They felt
the need to adapt Lemmers measures in order to accommodate the Twenty20
version of the game and to allow for a better understanding of statistics in short
series games where data may only be gathered from a handful of performances per
team. Lemmer went on to use the methods generated by Basevi and Binoy (2007)
and tried to incorporate it into an analysis of the first T20 World cup series. His study
(Lemmer, 2008) found that Basevi and Binoy’s methods of measuring performance
weren’t effective in short series games. Lemmer (2008) stated that ‘The desire to
5
adjust measures for use in Twenty20 cricket will not be realized as soon because a
lot of additional data must become available to do the work properly.’ The main
reason for this being that due to the fact it was the first World Series in this format of
the game, there wasn’t much data to compare and contrast the results to. Lemmer
also showed how through using his own method that was previously defined for
limited overs cricket, a different measurement would have been generated than
those from the Basevi and Binoy (2007).
Further research into cricket has been carried out with certain degrees of success
(Barr & Van den Honert, 1998). Their study looked at trying to determine the success
of batting in one day cricket through the use of statistics. In a similar way to other
researchers looking at analysing cricket, they chose to create a formula in order to
determine a profile of a successful player. Their formula included combined average
and consistency of scoring which allowed them to come up with different results as
opposed to other studies. They stated the need for continual updating of their theory
as the game of cricket is ever evolving and in order to keep up with new innovations,
the constraints need to be tested regularly. The majority of cricket research from a
statistical viewpoint looks to define what a successful team is, what they do and how
individuals can impact upon results. Cohen (2002), Gray & Le (2002) and Barr &
Kantor (2004) all aimed to create a forecast for the purpose of future matches and
tournaments. Cohen (2002) looked at analysing the winning chances of teams by
utilising career bowling and batting averages in order to predict the likelihood of a
bowling unit dismissing a batting side and how many runs a batting team was
capable of scoring. Gray and Le (2002) furthered this method to include a measure
which allowed for projected scores to be generated during the match. They did this
by using wickets remaining during the innings and current runs scored in order to
forecast the team’s final score
Barr & Kantor (2004) went on to justify the importance of ‘keeping up with the times’
in terms of performance analysis in cricket. Crickets ever altering environment has
meant that research collected from previous years may have measured success on
a different scale to more recent work. Measures are constantly being utilised and
updated so that data that is collected can be analysed on a scale that fits with the
current adaptations of the game. Lemmer (2011) summed this up when he stated
6
that ‘cricket player’s performances are influenced by many factors. It is very
important to create a method that allows performance to be compared in a fair way.
Researchers must persevere in the creation of more realistic measures’. It can be
clearly observed that previous statistical research in cricket has been utilised by
more recent researchers. Adaptations to Lemmer (2002) combined bowling rate
study have been used in more recent times by Basevi and Binoy (2007). This shows
how methods that worked in previous years can still produce quality data for
analysis, however to further improve the data collected, measures and variables may
have to be adapted to fit into the current cricket environment.
Gerber & Sharp (2006) looked at trying to analyse performance from a different
perspective. They did use formulas that tried to identify what success was for
individual players however they aimed to produce a programme that was able to
select an optimal squad for a one day international series. Instead of attempting to
forecast future results and performance, they aimed to create a selection criterion
which players had to meet to be selected. By using this programme they emphasised
the fact that it moved subjectivity from the study, which meant that selector’s
opinions were kept apart from the actual optimum squad. Previous studies have
shown that selectors don’t always follow the results that are given (Lemmer, 2008).
This is down to their own subjectivity and views on individual players whether it be
technically, statistically or personally. In order for results to be appropriate it is
important that the researcher creates operational definitions that can be objective
and results that haven’t been generated through the opinions of individuals.
(O’Donoghue, 2010) stated that ‘operational definitions in performance analysis are
often vague and it may not be possible to concisely present completely objective
definitions’ showing how difficult it can be for an observer to remain objective
throughout a study. Objective judgement becomes effective in performance analysis
when throughout the study the observer interprets the data and uses views solely of
their own without being compromised by other factors such as previous experience
or others opinions. It is important in cricket that the data collection doesn’t have any
bias towards players, which shows how by using statistical data, bias can be
removed as the results are clear and opinion doesn’t affect how the statistics are
interpreted. Gerber and Sharp (2006) ensured that the data was non-biased and
when feedback was given to the coach, there was an allowance for choice
7
constraints to be manipulated and generated by the coaches. This proved to be a
success as it allows for the coaches to make their own decisions and means that the
research and methods used created non biased data.
2.3 – Justification of Study
The study in question will aim to create new methods and measures of how
performance analysis in cricket can be used and how success can be observed,
created and sustained. This study is justified as previous researchers have stated
the lack of empirical data (Petersen et al, 2008b) as a downfall in performance
analysis in cricket. The results that are generated have to be useful in a modern day
context so that data can be kept up to date and variables can be defined in
correlation with the ever changing environment that cricket evolves in (Petersen,
2008a ; Barr & Kantor, 2004). Previous analyses have touched upon some key areas
in cricket research. By looking at trying to forecast future performances and results
some formulas and methods have been further developed already. The need to
delve deeper into the statistics in order to find unforeseen areas that can impact on
performance has been spoken about (Lemmer, 2011). In order to build on previous
research it is important to stick to the key principles of analysing cricket performance.
But in order to keep updated statistics and more realistic measures it’s important to
study the data in new ways.
Petersen et al (2008b) identified the 3 key areas of cricket as bowling, batting and
fielding and analysed performances including those parameters. The data that was
generated from his research was fairly basic, summarising that winning teams had
higher run rates and more 50 run partnerships. He found that team selection should
focus on wicket taking bowlers at the beginning of the innings and bowlers who can
restrict runs at the end of an innings, he also showed the need for scoring more
boundaries in the powerplay overs and a better rotation of the strike for his
determining factors of success. In terms of Petersen et al (2008b) comment about
usefulness of statistics, it is hard to see how the data which was collected during the
study would have offered any new insight into performance at that level. Coaches
are highly qualified and the data that was generated was fairly general to cricket with
8
no real specific focus on why such data occurred. The study that will be used will
focus on looking into areas that may be the cause of the performance measures
identified by Petersen (2008a; 2008b) and try to make sense of how areas further
into the data can have a larger relevance to the outcome of the statistics.
Previous research in cricket has allowed for studies to be replicated and utilised
furthering the statistics that can be created. Success has been generated through
the use of the previous methods as performance indicators are highlighted and some
breakthroughs have been made in creating models of what performance analysis is.
It has allowed for further methods to be developed and has impacted on this study
due to the fact that the performance measures haven’t gone deep enough into the
statistics in order to find the value and meaning of what is being measured and why
the data has occurred. The aim of the study is to find other areas in cricket that can
create success and allow for results to be created that can be followed to maintain a
model for continued success.
9
CHAPTER THREE - METHODS
3.1 – Research Design
The aim of the study is to analyse multiple performance indicators and variables in
cricket to understand whether they have an effect on the outcome of successful
performance. In order to avoid analysing at a surface level of basic descriptive
statistics it is important to choose variables that look past the initial indicators that
were identified by Petersen et al (2008b) and look further into areas that may have
caused the indicator values to occur. Eighteen One day international (ODI) matches
from four different ODI series were observed and data was input using an (Nx
Cricket Scorebook) app designed for iPhone and iPad use. All of the data that was
collected was then analysed with an emphasis put on the decided performance
indicators in order to see how they compared and differentiated within each series
and how each series compared to another. In order to compare and contrast the
data a chi-square test was used to test relationships between nominal variables and
a Kruskal Wallis H test followed by a Man Whitney U test were utilised to test the
differences between numbers of independent samples. All of these tests were used
due to the non parametric nature of the data.
3.2 - System Development
A pilot study was used in order to test the reliability of the data that was being
inputted into the system and how consistent the observer was. Reliability in
performance analysis is the ability to ‘seek to evaluate the consistency with which a
method can be used’ (O’Donoghue, 2010). In order to assess reliability an intraobserver reliability test was used. An intra reliability test was used in order to
compare the nominated variables from both pilot studies. O’Donoghue (2010) stated
that ‘an intra-observer reliability test can be useful during the pilot study of a system’.
To complete the test the observer watched an ODI match between Australia and Sri
Lanka and input the data into the (Nx Cricket Scorer) application. Once the initial
study was completed, a day’s gap was included before analysing the same match
again. Following the same process as before data was collected in order to test how
reliable to observer was. Both sets of data were inputted into the Test system under
the variables (runs scored, batsmen totals, bowling figures, fielding outcomes, run
10
rates, cumulative runs & scoring patterns) values. After transferring the data from
both studies into the system it allowed for a Kappa value to be generated. ‘Kappa
determines the proportion of points that are agreed by the operator excluding the
proportion that could be agreed by chance’ (O’Donoghue, 2010). Previous studies
have used kappa scores in order to produce a better understanding of how reliable
results were (Choi et al, 2007). They found that by determining kappa values an
acceptable value of error could be concluded allowing for results to show a better
representation of the data inputted. The Kappa valued that was generated from the
pilot study was then compared to Altman (1991) scale of agreement. Robinson and
O’Donoghue (2007) stated that kappa is a useful reliability statistic to use with the
nominal variables.
3.3- Matches (Participants/ Performances)
The sample used in the study was 4 ODI series between a selection of one day
cricket playing nations (India vs. England; India vs. West Indies; Bangladesh vs.
Pakistan & South Africa vs. Sri Lanka). Due to the aim of the study looking at finding
trends and patterns in successful performance it was important to look at series
rather than individual games. By analysing series performance it provides more
scope for the analysis as performance trends can be noticed during the matches, but
then also cross compared between series. It also allowed for a better understanding
as to why teams achieved success as an analysis could be done on the basis of a
number of performances. Out of the four series that were looked at, there was at
least one ODI side that was ranked in the top 4 world rankings. This meant that
generalised successful sides could be analysed in order to see what makes them
successful, but also highlighted how other teams may have performed in order to
achieve success against the higher ranked teams.
11
3.4 – Data Gathering
The method used to collect the data was relatively
simple and easy to use. After the original Kappa score
test results, it was clear that both the method and the
observer produced consistent results that were valid in
the study. In order to collect the data an application
called Nx Cricket Scorer on iPhone was used. The
application offers a variety of functions that can further
statistics from cricket scoring. The process of entering
the data into the system is simple; there are a number
of function buttons for each delivery ranging from
amount of runs scored to wickets taken (Fig.1). Further
Fig. 1 Functions of NX Cricket
Scorer Application
functions allowed for data to be entered for where the runs were scored and how the
wickets were taken. At the end of the game the data is transferred into a PDF format
and generates statistics in a match profile from the game.
These statistics include scorecards; manhattans; cumulative run rates; wagon
wheels for bowlers and batters and series statistics for
each individual player (Fig.2). By generating such a
wide variety of statistics it was simple to analyse the
data and take the data which was identified as useful by
the performance indicators. After the results were
complete, each PDF file for each game was printed in
order to produce a match report. The players overall
statistics were collected and printed upon the
completion of the series. Once all of the data had been
Fig. 2 An example of data output from
series statistics
collected the key performance indicators could be
identified and data analysis could then begin.
12
3.5 – Data Analysis
The performance indicators that were used were focused around the key aspects of
cricket. In order to generate a better understanding of successful performance a
deeper analysis into the cause of outcomes was used. Bowling effectiveness was
looked at from a perspective of how many wickets were taken by different types of
bowlers, looking at how spin or seam bowlers can affect the outcome of the game.
Strike rates, wickets taken and overs bowled were analysed from the basis of series
statistics in order to determine how effective each type of bowler were. A comparison
was also made between overs bowled by each type of bowler in powerplay overs
and how many wickets were taken. From a batting perspective the general run rates
and average were utilised in periods of the batting line up and periods of the game.
Other areas that were analysed were looking at run rates in five over periods after a
wicket had fallen and the ability to rotate the strike with singles and also the
percentage of runs that each area of the batting sides scored in relation to the
number that they batted. As an overall comparison between both areas an analysis
took place of the powerplay overs in order to determine whether they were batter or
bowler dominated.
In order to analyse these indicators there were 2 statistical tests that were used. The
first two tests that were utilised were the Kruskal Wallis H and the Mann Whitney U
tests which looked at analysing a number of the indicators from each series sample.
This was deemed effective as it can be used to test differences between 3 or more
non parametric samples (O’Donoghue, 2010). The test incorporated the statistics
well as there were 4 sets of series samples to analyse an also incorporated the
individual variables from each series for a cross comparison. The other test that was
used in order to discover trends was a Chi-Square test. This test finds relationships
between nominal variables (O’Donoghue, 2010) and allows them to be compared
across a series of grouping variables. The data that was collected was input into
both the test systems so that values for the statistics could be ranked in terms of
importance as to what makes a team successful. The validity of the use of both tests
was justified as the variables that were analysed represent the important concepts
13
being measured (Morrow Jr et al., 2005: 82). Both Petersen et al (2008b) and
Lemmer (2005) stated the need to further develop methods of analysing cricket
within the batting and bowling framework and this is what this study aims to achieve.
3.6 – Reliability
A key area in any aspect of performance analysis is the reliability of the study and of
the data produced. O’Donoghue and Longville (2004) stated that it is important that
performance analysis is ‘undertaken reliably and accurately in coaching contexts
because of the decisions that are made by players and coaches using the
information provided’. Performance indicators were utilised and developed from
previous studies and proved reliable as they were ‘independent of the opinion of
individual operators’ (O’Donoghue, 2010).The performance indicators that were
being used were focused around the key aspects of cricket and were analysed using
systems that have been used in the past. In order to assess the reliability of the data
and the methods an intra-observer reliability test was used during the pilot study.
The score that was generated from both areas of the pilot study was 1.00 which on
Altman’s (1991) scale showed a perfect level of agreement between both
observations. All results that generated from the pilot study matched meaning that
the observer reliability was very high. This was down to the nature of the Nx Cricket
Scorer application and its easiness of data input and also the fact that after each
delivery in cricket there tends to be a short amount of time to check that any
observations were correct. By producing such a high intra-agreement score the
reliability of the application and the observer were deemed to be acceptable enough
to continue use throughout the rest of the study, meaning that the method used in
the study didn’t need to be changed as all of the data that was produced could be
utilised to look at the performance indicators that were identified.
The chi-square test and Kruskal Wallis H Test have been used in the past, and as
shown in chapter 3.5, have been deemed reliable for discovering the results this
study aims to collect.
14
CHAPTER FOUR - RESULTS
As previously discussed the data that was collected from the analysis was input into
a Chi-Square and Kruskal Wallis H Test in order to test relationships and trends
between sets of nominal variables from each series. The data was collected from 4
series so a cross comparison was made between the winning and losing sides from
each set of matches. The data that was collected also allowed for a comparison
between various performance indicators across each series to be examined in order
to asses which areas are key for improving success rates in cricket, and also to
observe what areas set the ‘benchmark’ for what international cricket teams should
be striving to achieve.
In order to compare the 4 sets of data the results were put into graphs and tables
which show how each performance indicator that was deemed acceptable as an
area of interest compared and contrasted with eachother. The analysis was split into
4 areas. General Overviews which looked at comparing the basic series statistics
against eachother, powerplay comparisons which looked at varying performance
indicators results within the powerplay overs, and finally the rebuilding phase, which
looked at a period of 5 overs after each wicket and performance indicators which
occurred during this phase. The final area of results was the data that was generated
from the non parametric tests in order to highlight the significant variables. For the
purpose of the results the outcomes of the each series were as follows:

India (IND) v England (ENG) (India win 5-0)

Bangladesh (BAN) v Pakistan (PAK) (Pakistan win 3-0)

India (IND) v West Indies (WI) (India win 3-2)

South Africa (SAF) v Sri Lanka (SL) (South Africa win 3-2)
4.1 – General Overview
The initial area that was analysed was the effectiveness of the batting line up. In
order to analyse this, the data was split into 3 areas. The top 3, numbers 4-7 and the
tail end (numbers 8-11). This was done in order to observe whether there was a key
area of performance which aided successful sides.
15
70.0%
Percentage of Run Scored
60.0%
50.0%
40.0%
Top 3
30.0%
Number 4-7
Number 8-11
20.0%
10.0%
0.0%
IND
ENG
BAN
PAK
IND
WI
SAF
SL
Teams
Fig.3. Comparison of runs scored by different areas of the batting line up
The table (Fig.3) clearly highlights how the winning teams from each series had a
higher percentage of runs scored by the middle order (numbers 4-7) in comparison
to their opponents. There was also a significant difference between the amounts of
runs scored by numbers 8-11, with all losing teams scoring a higher percentage of
their overall runs during these batting positions. This is caused by the fact that
numbers 8-11 of the losing sides were often exposed earlier in the game than the
winning sides meaning the opportunity was there for those batters to score more
runs.
The next area that was analysed was the amount of overs that seam bowlers and
spin bowlers were utilised for during each series. This was analysed in order to
observe whether a series was dominated by spin or seam and how effective the
individual type of bowler was.
Figure 4 clearly identifies that three of the winning sides bowled more overs of spin
over the series of matches in comparison to their opponents. In order to analyse the
effectiveness of both spin and seam bowling during the series, a table was created
showing the key performance indicators of both spin and seam bowlers throughout
each series (Table 1).
16
Overs Bowled
180
160
140
120
100
80
60
40
20
0
Seam
Spin
IND
ENG
BAN
PAK
IND
WI
SAF
SL
Teams
Fig.4. Comparison of overs bowled by spin and seam bowlers during the series
Table 1. Comparison of bowling series statistics between spin and seam bowlers
IND
Wickets Taken - Seam
Bowlers
Strike Rate - Seam
Bowlers
Wickets Taken - Spin
Bowlers
Strike Rate - Spin
Bowlers
19
46.9
23
ENG
BAN PAK
16
7
61.3 33.4
6
6
IND
WI
16
27 31.7 45.3 25.4
30.3
21
29.9 120.7 33.1
26.1
21
21
SL
22
15
18
SAF
8
9
4
31 55.4 26.8 100.8
Table 1 has been used to back up the statistics represented on Figure 4. The data
clearly shows that not only did the winning team’s bowl more overs of spin, the spin
bowlers from the successful sides were a lot more effective than the losing sides in
terms of wickets taken and strike rates. The amount of wickets taken and the rate of
which they gain wickets could be a key factor in the decision to bowl more overs of
spin. Interestingly out of the 4 series, the winning sides all had better strike rates
from their seam bowlers than their opponents, meaning that through use of spin and
seam the rate at which wickets were taken was much quicker.
17
4.2 – Powerplay Comparison
The next area that was analysed was the powerplay overs as they were deemed as
an area that can affect the outcome of a game. The first area that was analysed was
Average Dot Balls Faced
the amount of dot balls that were faced by batsmen during the powerplay overs.
90
80
70
60
50
40
30
20
10
0
77.7
61.4
75.4
70
62.8
59
57.6
62.4
Dot Balls
IND
ENG
BAN
PAK
IND
WI
SAF
SL
Teams
Fig.5. Average dot balls faced during powerplay overs
Figure 5 shows that during the powerplay overs, the winning teams faced less dot
balls than their opponents. Figure 6 links into this as a relationship between dot balls
faced and wickets lost could potentially be found.
Average Wickets Taken
4
3.5
3.8
3.7
3
2.5
2.8
3.6
3
3.6
3
2
2
1.5
1
0.5
0
IND
ENG
BAN
PAK
IND
WI
SAF
SL
Teams
Fig.6. Wickets taken during powerplay overs
18
Wickets Taken
Figure 6 shows that each successful side took more wickets during the powerplay
overs showing that there is a link to the amount of dot balls faced and the amounts of
wickets lost. What Figure 6 also highlights is that each winning team took at least
three wickets during the powerplay overs, which can cause more pressure during
decisive periods of the game.
Another area that can be looked at during the powerplay overs is the amounts of
overs bowled by spin and seam during this particular period of the game. A table
representing the average data from the series of how many overs were bowled by
Percentage of Overs
Bowled
spin and seam is utilised (Fig.7)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Spin
Seam
IND
ENG
BAN
PAK
IND
WI
SAF
SL
Teams
Fig.7. Bar chart identifying the use of spin or seam bowlers during powerplay overs
Figure 7 identifies that in three of the series, the winning teams bowled utilised their
spin bowlers more during the powerplay overs. In the case of South Africa, who used
their seam bowlers to negotiate the powerplays, Table 1 can be utilised to observe
the effectiveness of the South African seam bowlers, to try to understand why their
bowling during powerplays was seam dominant.
4.3 – Rebuilding Phase Comparison
The rebuilding phase consists of a 5 over period after the loss of a wicket. The data
that was analysed looked at how teams set about rebuilding, whether that be setting
a platform by manipulating singles or whether they went about scoring in a more
aggressive way. In order to put this data into graphs each performance indicators
19
deemed as a key area to highlight was compared across each series from a
perspective of how all teams reacted to the loss of wickets from the 1st to the 9th.
The first area that was looked at was the average amount of runs scored during the
5 over period after each wicket was lost. In order to compare an average was taken
across all series and input into the table (Fig.8; 9). The graphs were split between
the winning and losing sides in order to observe trends of patterns in the data.
40
Average Runs Scored
35
30
Fig.9
25
IND
20
PAK
IND
15
SAF
10
5
0
1
2
3
4
5
6
7
8
9
Teams
Fig.8. Comparison of average runs scored during 5 over period after initial wicket loss (Winning
teams)
40
35
Average Runs Scored
30
25
ENG
20
BAN
WI
15
SL
10
5
0
1
2
3
4
5
6
7
8
9
Teams
Fig.9. Comparison of average runs scored during 5 over period after initial wicket loss (Losing teams)
20
Figure 8 shows a negative correlation between average runs scored after the loss of
each wicket. This can be caused due to the fact that the winning teams often didn’t
reach the lower order batsmen before they won the game, meaning that fewer
wickets were lost during each period. Figure 9 shows that the losing teams tended to
accelerate their average runs during the middle order. This can be caused by the
situation of the game, whether they are chasing a big score, or because of the
mentality of the players in the middle order who may be looking to score at a quicker
rate. Figure 9 also shows that the losing sides also scored more runs towards the
end of an innings during the 5 over period; again highlighting that this could be
caused by the mentality of the lower order batsmen.
Another area that was analysed was the ability of successful and non-successful
teams to manipulate singles during the 5 over rebuilding phase after losing a wicket.
An average was taken from how many singles were scored during the period after
each wicket was lost throughout the series and put into 2 graphs showing how the
general trends of winning and losing sides compared.
16
14
Singles Scored
12
10
IND
8
PAK
6
IND
4
SA
2
0
1
2
3
4
5
6
7
8
9
Wicket Lost
Fig.10. Average singles scored during a 5 over period after a wicket (Winning teams)
21
14
Singles Scored
12
10
ENG
8
BAN
6
WI
4
SL
2
0
1
2
3
4
5
6
7
8
9
Wicket Lost
Fig.11. Average singles scored during 5 over period after a wicket (Losing teams)
Figure 10 shows that winning teams tended to score the majority of their singles at
the beginning and middle order of the batting line up. The tailing off towards the end
again suggests that the batsmen didn’t always get a chance to bat due to the fact
that their team may have already won the game.
Figure 11 shows how the losing sides scored a lot more singles towards the end of
the innings than the winning teams. Again this could be down to more exposure or
the fact that the field is often more defensive towards the end of a game allowing for
more gaps to be exploited. It also highlights that both winning and losing teams
struggled to score singles after the first wicket, which again could be influenced by
field placing. The final area that was analysed was the amount of wickets that were
Average Wickets Lost
lost during the 5 over rebuilding period.
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
7
8
9
IND
0.8
0.6
0.2
0.2
0
0.4
0.2
0
0
PAK
1
1
0.3
0.6
0.6
1.3
1
0.3
0.3
IND
0.6
0.6
0.8
0.8
0.6
0.4
0.2
0.6
0.2
SA
0.4
0.2
0.8
1.2
0.8
0.4
0.2
0
0
Fig.12. Average wickets lost during 5 over period after initial wicket (Winning teams)
22
2.5
2
Average Wickets Lost
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
ENG
1.2
0.6
1.2
1.6
0.8
1.4
1.8
1.4
0.8
BAN
1
1.3
2
0.7
0.3
1.3
2
1.3
0.7
WI
0.8
1
0.8
0.4
0.2
0.8
0.6
0.6
0.2
SL
1
0.6
0.6
1.4
1
0.8
0.8
0.4
0
Fig.13. Average wickets lost during 5 over period after initial wicket (Losing teams)
Figures 12 and 13 show how the winning teams on average lost fewer wickets that
their opponents during the rebuilding phase. The losing sides tended to lose a lot
more wickets in quick succession through the series during the beginning and middle
period of the innings. They also lost more wickets during the later stages; however
this can be caused by more exposure, which the winning teams didn’t experience.
By proving that wickets fell at a quicker rate and more frequently during a spell just
after a wicket, it can be proven that rebuilding is justified as a key area to look at as
by rebuilding an innings, it takes away the pressure of early wicket losses and
decreases the likelihood of the tail end being exposed early in the match.
4.4– Non-Parametric Statistics
In order to gain a statistical insight into the results that were collected a chi-square
test and a Mann Whitney U test were utilised. The chi-square test analysed all
performance indicators and grouped the results into successful and non-successful
areas. On the back of these results a Man Whitney U test was conducted in order to
test the significance of the variables and highlight the areas that were important
between the winning and losing sides.
23
Table 2. Results from Mann Whitney U Test highlighting key areas of significance (P>0.05)
Ppwickets
.000
Sratespin
.000
Batav
.000
Doteight
.500
Wilcoxon W
10.000
10.000
10.000
10.500
Z
-2.337
-2.309
-2.309
-2.191
Asymp. Sig. (2-tailed)
.019
.021
.021
.028
Exact Sig. [2*(1-tailed
Sig.)]
.029a
.029a
.029a
.029a
Mann-Whitney U
Table 2 represents the four significant variables that were highlighted by the Man
Whitney U Test. All 4 results had a value of P>0.05 which is deemed as a high level
of significance. The test results were between both winning and losing groups and
the four variables in figure 15 were the only variables with values P>0.05.
24
CHAPTER FIVE - DISCUSSION
5.1 – General Overview
In order to fully analyse the results it is important to look beyond the outcomes and
analyse why the data that was collected from each performance indicator may have
occurred. In cricket there are many areas that can affect the outcome of a match,
however pin pointing the decisive areas are what all cricket analysts aim to discover.
The outcome of successful sides can often be compared between two opponents
during a series, however in order to understand what successful sides continuously
do to reach their level of performance it is important to understand how they go
about it over a sustained period of time. By comparing the outcomes from 4 separate
series trends and patterns can begin to be developed allowing for a greater
understanding of how the teams win games. However due to the nature of cricket, if
the data shows areas of no difference, this can still be utilised in performance
analysis by using the data collected as a benchmark for what successful sides
should set out to achieve.
From the first set of results from the general overview it is clear to see that the
winning teams scored a higher percentage of their runs during the middle order.
However from viewing Figure 1, it can also be recognised that the percentage of
runs scored by the top 3 from both winning and losing sides tended to be a lot closer
together in terms of comparison. An area that can be brought up from this is not only
the ability of the middle order, but also the reliance each team puts on their numbers
4-7 and how they deal with that pressure. In the case of South Africa, a higher
importance was put on the top 3 batters which lead to them scoring less runs than
Sri Lanka in the middle order during the series. When questioning the outcome of
areas of the batting line up many factors can account for the percentage of runs
scored. When comparing India and England during their series, a large gap can be
seen between the percentages of runs scored by the middle order with India scoring
considerably more. With this in mind, by linking the data to figure 5 a better
understanding of why this may have occurred can take place. The middle over’s of
an innings tend to be bowled by spin bowlers and when you compare the strike rate
of the England spinners (120.7) and the India spinners (29.9) it’s clear to see how by
losing less wickets a team can score more runs. From England’s perspective the
loss of wickets during the middle order impacted upon their ability to score as more
25
pressure is applied after each wicket is lost. Petersen et al (2008b) aimed to analyse
the effectiveness of bowling by using a formula including wickets taken, runs scored
and over’s bowled. In order to further this in comparison with this study, an
allowance could be included for how the bowlers bowled during phases of the game
to understand why runs are scored at during certain periods of the batting line-up.
The effectiveness of bowlers has been widely researched within studies by Petersen
(2008a; 2008b) and Lemmer (2002; 2005; 2006; 2008 & 2009). During this study the
effectiveness was looked at in terms of how many over’s each team utilised their
varying types of bowlers, and how effective these bowlers were based on wickets
taken and strike rates. These two areas were highlighted as the main aim of a
bowling unit is to dismiss a batting side for as few runs as possible. In order to
assess the effectiveness spinners and seam bowlers were split up to observe how
often the two variants were used and how effective they were.
The statistics from Figures 3 & 4 that stand out are the fact that successful sides
utilised their spin bowlers more during the series at a higher success rate than the
losing teams. Another interesting point was that the strike rates of the winning teams
seam bowlers was higher than the losing teams despite bowling less over’s. During 3
series (India v England; Bangladesh v Pakistan & India v West Indies) the amount of
spin that was bowled and the success rates of those bowlers was substantially more
in the winning teams. A key point that could explain these results is the fact that
these series took part in the sub-continent. The sub-continent is renound for its
conditions that are conducive for spin bowling and also the production of quality spin
bowlers. In the case of India who bowled a lot more over’s of spin in comparison to
their opponents (England & West Indies) their bowlers had been brought up on
pitches that spin and would have trained and bowled in the conditions for the
majority of their careers. Figure 5 highlights that Bangladesh and Pakistan used their
spin bowlers for similar amounts during the series. Despite the fact that Bangladesh
were the home nation, Pakistan is also a sub-continent side so would have had
similar experiences whilst learning to play so their spin bowlers would have had a
good understanding of the conditions and wouldn’t have to adapt as much as a
country from outside the sub-continent. English and West Indian conditions are at
opposite ends of the spectrum in terms of bowling spin so their failure to adapt to
conditions could have been a key factor in their defeats. Interestingly during the
26
South Africa and Sri Lanka series, the sub-continental side (Sri Lanka) bowled the
same amount of overs of spin as South Africa yet had lower success rates. This
again can lead to the assumption that the South African spinners may have
understood their southern hemisphere conditions better and adapted quicker than
the touring team. Due to the majority of the data from this study being taken from the
sub-continent, further research can be explored in terms of how the away teams
spinners adapt, and also how varying conditions can affect the outcome of matches.
This further research would need to be carried out over a longer period of time and
analyse how effective each nation is in terms of their spin and seam bowlers on
varying continents.
5.2 – Powerplay Comparison
The powerplay comparison data has some interesting insights into performance
during the particular periods of the game. Figure 6 identifies that all of the successful
sides faced less dot balls during the powerplay overs. Figure 7 links nicely into the
powerplay statistics as it allows for a pattern to be observed. Not only did the losing
sides play more dot balls than the opposition, but they also lost more wickets. All
winning teams averaged over 3 wickets each during the powerplay overs throughout
their series. Both figures suggest that there could be a key trend between facing
more dot balls and losing more wickets during the powerplays. This trend can be
explained from a cricketing perspective by understanding what dot balls do. Woolmer
et al (2008) identified that ‘during intense situations dot balls can increase pressure
on batsmen and often lead to them losing focus and getting out’. This would explain
why more wickets were lost during each period in unsuccessful sides. The pressure
that facing more dot balls creates can ultimately lead to a higher dismissal rate.
Petersen et al (2008b) highlighted the need for ‘rotation of strike’ as a key area of
successful performance. In this case he is justified as by rotating the strike, less dot
balls are consumed and the changes in batsmen through scoring more singles can
force the bowling team to change their game plan or at least put them in a less
comfortable position.
Another area during the powerplay that was highlighted was the use of spin bowlers
during this particular part of the game. 3 out of the 4 winning teams used a higher
ratio of spin bowlers during the powerplay period. Again in a similar way to the use of
27
varying types of bowlers throughout a series, this can be explained by a number of
factors. The conditions, as previously highlighted, can mean that spinners are more
effective and harder to score off during areas where a batting side is looking to
accelerate. By observing Figure 5 again, the spin bowlers for the winning sides were
more effective in terms of wicket taking, and this can link into the fact that the
winning teams took more wickets. By creating the pressure from dot balls the
bowlers can capitalise and be more aggressive in terms of looking to take wickets.
Another aspect that has already been considered is the conditions. The 3 successful
sides were again, sub-continental sides in sub-continent conditions. South Africa
again favoured the use of seam bowlers during powerplays which was caused not
only by their effectiveness but also by the South African conditions being more
conducive to seam bowling. In order to accept the correlation between dot balls
faced and wickets lost during powerplay overs, a more general comparison
incorporating all continents could be utilised.
5.3 – Rebuilding Phase Comparison
An area that was considered valuable in cricket performance was the ability to
rebuild after the loss of a wicket. This area was analysed by comparing the
effectiveness and dynamism of the winning and losing teams during a 5 over period
after losing a wicket. In order to analyse trends and patterns, the successful sides
were split from the unsuccessful sides when graphs were created. Figure 9 shows
how the average amount of runs scored during the 5 over period varied between
each wicket. There is a negative correlation between the four winning teams showing
how less runs were averaged as the loss of wickets increased. This can be
explained by the successful sides not always needing their lower order batsmen due
to matches already being won. Due to their success, less exposure is applied to the
lower order batsmen meaning that there is less chance for runs to be scored and
also less chance of the batsmen actually being required to bat.
From the opposite perspective, the losing sides tended to increase their average
scores during the middle and later order of the batting line up (Fig.10). This can be
caused by the state of the game, for example, if a team is chasing a high score and
lose early wickets. Or can be due to more exposure meaning that the batsmen have
longer at the middle to express themselves. Due to the less dominant ability of the
28
lower order batsmen, the mentality of these players tends to be more aggressive,
which would explain how during the latter stages of the innings, more runs were
scored after the loss of each wicket.
From Petersen et al (2008b) highlighting the importance of rotating the strike for a
successful outcome. An analysis was completed on how many singles were scored
during the pressure stage of losing a wicket. Figure 11 shows how a key area for the
successful sides after a wicket was the period after wicket number 4 and 5. The
successful sides averaged around 8 singles per match which allowed for strike to be
rotated. Interestingly two of the losing sides (Sri Lanka & Bangladesh) averaged 10
singles after wickets number 4 and 5. In order to explain this it is important to look at
the location of the sides. Sri Lanka and Bangladesh are both sub-continental sides
and despite the Bangladeshi’s being at home for their series, the Sri Lankan’s
managed to rotate the strike through the use of singles in South African conditions.
This highlights the ability of the sub-continental players to score runs in a 360 degree
radius, and also emphasises their ability to hit gaps in the field. During wickets 4-5 to
field tends to be spread as middle order batsmen are normally engaged in the middle
overs of the game, however Figure 12 does show how losing sides can still rotate
the strike during phases of the games at a good rate.
An area that is similar between both sides is the lesser amount of singles scored
after wickets 1 and 2. This can be caused by the fielding setting being more
aggressive at the start of a match leading to fewer fielders saving boundaries which
can decrease the amount of gaps available to score singles and rotate the strike.
Figures 13 and 14 both show the amount of wickets lost during the 5 over rebuilding
phase after the loss of a wicket. The main area that these two graphs highlight is the
difference between the wickets lost after the fall of wickets 1, 2 & 3. The successful
sides didn’t lose as many wickets as the losing sides during the 1st and 2nd wicket
loss rebuilding period. Furthermore there is a substantial difference between the
amounts of wickets lost after the 3rd wicket falls. These differences can be caused by
varying factors. The initial factor that has to be considered is the ability of the bowling
side as an aggressive wicket taking unit. Another area that can account for these
results is the ability of the batting line up and how much emphasis is put on the
batting side. For example, as highlighted earlier, South Africa had a higher reliance
29
in terms of run scoring percentages on their top 3. The top 3 of South Africa were
their consistent performers meaning that losing those wickets early could affect the
rest of the side throughout the match. In India’s case, they tended to rely on the
middle order batters as the skill level shown and applied by the players in those
positions justified them scoring more runs. Therefore losing early wickets for India
was not as big an issue as losing early wickets for South Africa.
Another area the figures highlighted was the ability of the middle order. In the losing
teams data more wickets are lost during these areas as teams tended to collapse
during the games. Successful sides tended to lose wickets during the middle order
period, however at a lesser rate throughout the duration of the series. Interestingly
from all data collected from batters numbers 1-8, not one value was 0 from any of
the series averages. This highlights an area for teams to become more successful by
training and preparing game plans for how they go about rebuilding an innings.
5.4 – Non Parametric Statistics Comparison
In order to fully understand the data the research was compared from a statistical
approach. The use of the tests identified in the methods section of the study further
increase the understanding of which variables are key and also highlight areas that
can cause sustained success.
The four significant variables that were highlighted by the Mann Whitney U test were
interesting and gave statistical backing to key areas that were highlighted during the
results (Fig.15). The Mann Whitney U test identifies any variables with a significance
value of P = <0.05 as a key area of significant difference. The four variables that
were identified by the system has significance values of P = .029 which from the
systems rating, justifies them as significant.
Wickets lost during the powerplay were the first variables that were deemed
significant by the non parametric tests. The previous data that was produced with
regards to wickets lost during the powerplay showed how the successful sides took
more wickets during these periods of the match. Losing more wickets during
powerplay overs can be detrimental to the team in a number of ways. The mentality
of powerplay overs is to be more aggressive and scoring opportunities are increased
as the rules regulate the amount of fielders protecting the boundary. Through losing
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a wicket in this period it is simple to see how the mindset of batters would change.
Whilst trying to rebuild the innings with the field close in, it become harder to rotate
the strike and score consistently. This can lead to more pressure being applied to the
batsmen and cause for lapses in concentration leading to wickets. The statistics from
the test have further justified this area as key in what makes a team successful.
The strike rate of spinners was also highlighted stating that there was a key
significance between the effectiveness of spinners during the series. As justified
before, the majority of the sample matches that were analysed were in the subcontinent and spin bowlers have a massive part to play in the outcome of the result.
The sides who won series used more overs of spin throughout than their opposition
which combined with their strike rate allowed them to take wickets at regular
intervals throughout the game. Batsmen’s series averages were also deemed as
significant by the test. Previous studies have looked at utilising batting statistics in
order to create performance benchmarks and rankings (Lemmer 2002; 2005 & 2006;
Barr & Van den Honert, 1998). These previous studies have been justified in their
use of the batting average statistic as a performance indicator of success. As
highlighted during the justification of the study, the main aim was to delve deeper
into statistics and to find a meaning as to why statistics occur. However as a simple
statistic in terms of batting performance, it is seen as a key area of significance
within successful performance.
The final area that was highlighted was the amount of dot balls faced after the 8 th
wicket fell in the 5 over period subsequent to it. The significance for this variable can
be explained by the situation of the game or the ability of the bowlers at the end of
an innings. The losing sides faced more dot balls after the 8th wicket fell due to the
fact that in most cases the matches were already decided and no impact could be
made by the lower order batters. The data can also be explained by the winning
sides facing fewer deliveries as a whole after the 8th wicket due to matches already
being won, so the ability to face more dot balls was taken away from them. However
despite the justification this area was highlighted from the tests, and was included in
the study due to its significance value. In order to further explore the importance of
this value a deeper analysis of the amount of balls faced after each wicket during the
5 over period after could have been included.
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CHAPTER SIX - CONCLUSION
The study that took place aimed to allow for a deep evaluation of statistics over four
series performances. The key findings tended to be that all winning teams had
similar patterns in certain performance indicator areas. A key finding related to the
use of spin bowlers in ODI cricket, and highlighted how successful sides not only
utilise their spin bowlers more, but also have spin bowlers who are effective wicket
takers. The use of more overs of spin during powerplay overs by successful teams
throughout the series also suggests that spin bowlers can often be key when looking
to achieve victory. Due to the effectiveness of the winning teams spin bowlers, a
larger percentage of runs were scored by their middle order batsmen in comparison
to their opponents meaning that a better understanding and game plan of how to
play spin can allow a team to be more effective.
An important area that was highlighted and followed a pattern was that the losing
teams lost on average 3 wickets each during the powerplay overs. This created
pressure on other areas of the team to perform during different phases of the games
and ultimately affected the outcome. This difference was highlighted as significant
from the Mann Whitney U test that was conducted.
The aim of the study was to interpret statistics from four ODI cricket series in order to
highlight patterns and trends that occur in successful sides. By highlighting these
areas a performance profile can be generated based on what successful sides have
done in order to grant their status as the winning team. This study highlighted some
key areas and found relevance between the performance indicators that were
highlighted between each successful side. Future research can be conducted on the
effectiveness of the trends that were found and whether they represent the make-up
of a successful side over a period of time.
In spite of the trends and patterns of play that winning teams achieved throughout
the duration of this study it is important to realise the differences in conditions. Three
of the areas of data were taken from sub-continental series allowing for a
comparison to be made as to what successful sides do when travelling to this
particular area. Interestingly the series that was played in the southern hemisphere
still contributed similar areas of performance which allowed for the successful sides
to perform in the way that they did. Cricket is always evolving and teams are
developing new methods in order to achieve success. In order to fully understand the
32
concept of what makes a successful side; methods will need to be constantly
updated to understand the ever changing nature and dynamic of ODI cricket. Further
studies could look at aiming to find trends between successful sides all around the
world, as series wins may require different trends on varying continents
33
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